ORCID Profile
0000-0003-2453-0914
Current Organisations
University of Western Australia
,
CSIRO
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Publisher: Springer Science and Business Media LLC
Date: 15-02-2019
Publisher: IEEE
Date: 12-2010
Publisher: Diva Enterprises Private Limited
Date: 2011
Publisher: Cold Spring Harbor Laboratory
Date: 09-04-2018
DOI: 10.1101/298042
Abstract: Reference populations for genomic selection (GS) usually involve highly selected in iduals, which may result in biased prediction of estimated genomic breeding values (GEBV). In the present study, bias and accuracy of GEBV were explored for various genetic models and prediction methods when using selected in iduals for a reference. Data were simulated for an animal breeding program to compare Best Linear Unbiased Prediction of breeding values using pedigree based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single Step approach (SSGBLUP), where information on genotyped in iduals was used to infer a matrix H with relationships among all available genotyped and non-genotyped in iduals that were linked through pedigree. In SSGBLUP, various weights (α=0.95, 0.80, 0.50) for the genomic relationship matrix ( G ) relative to the numerator relationship matrix ( A ) were applied to construct H and in another version (SSGBLUP_F), inbreeding was accounted for while computing A -1 . With GBLUP, accuracy of GEBV prediction increased linearly with an increase in the number of animals selected in reference. For the scenario with no-selection and random mating (RR) prediction was unbiased. For GBLUP, lower accuracy and bias observed in the scenarios with selection and random mating (SR) or selection and positive assortative mating (SA), in which prediction bias increased when a smaller and highly selected proportion genotyped. Bias disappeared when all in iduals were genotyped. SSGBLUP_F showed higher accuracy compared to GBLUP and bias of prediction was negligible even with selective genotyping. However, PBLUP and SSGBLUP showed bias in SA owing to not fully accounting for allele frequency changes because of selection of quantitative trait loci (QTL) with larger effects and also due to high inbreeding rate. In genetic models with fewer QTL but each with larger effect, predictions were less accurate and more biased for selection scenarios. Results suggest that prediction accuracy and bias is affected by the genetic architecture of the trait. Selective genotyping lead to significant bias in GEBV prediction. SSGBLUP with appropriate scaling of A and G matrices can provide accurate and less biased prediction but scaling requires careful consideration in populations under selection and with high levels of inbreeding.
Publisher: Springer Science and Business Media LLC
Date: 12-09-2017
Publisher: Cold Spring Harbor Laboratory
Date: 28-04-2022
DOI: 10.1101/2022.04.26.488110
Abstract: Epistatic interactions can play an important role in the genetic mechanisms that control phenotypic variation. However, identifying these interactions in high dimensional genomic data can be very challenging due to the large computational burden induced by the high volume of combinatorial tests that have to be performed to explore the entire search space. Random Forests Decision Trees are widely used in a variety of disciplines and are often said to detect interactions. However, Random Forests models do not explicitly detect variable interactions. Most Random Forests based methods that claim to detect interactions rely on different forms of variable importance measures that suffer when the interacting variables have very small or no marginal effects. The proposed Random Forests based method detects interactions using a two-stage approach and is computationally efficient. The approach is demonstrated and validated through its application on several simulated datasets representing different data structures with respect to genomic data and trait heritabilities. The method is also applied to two high dimensional genomics data sets to validate the approach. In both cases, the method results were used to identify several genes closely positioned to the interacting markers that showed strong biological potential for contributing to the genetic control for the respective traits tested. hawlader.almamun@csiro.au
Publisher: IEEE
Date: 11-2011
Publisher: Cold Spring Harbor Laboratory
Date: 08-05-2023
DOI: 10.1101/2023.05.07.539781
Abstract: GWAS excels at harnessing dense genomic variant datasets to identify candidate regions responsible for producing a given phenotype. However, GWAS and traditional fine-mapping methods do not provide insight into the complex local landscape of linkage that contains and has been shaped by the causal variant(s). Here, we present ‘crosshap’, an R package that performs robust density-based clustering of variants based on their linkage profiles to capture haplotype structures in a local genomic region of interest. Following this, ‘crosshap’ is equipped with visualization tools for choosing optimal clustering parameters (ε) before producing an intuitive figure that provides an overview of the complex relationships between linked variants, haplotype combinations, phenotypic traits and metadata. The ‘crosshap’ package is freely available under the MIT license and can be downloaded directly from CRAN with R .0.0. The development version is available on GitHub alongside issue support ( acobimarsh/crosshap ). Tutorial vignettes and documentation are available ( jacobimarsh.github.io/crosshap/ ).
Publisher: Springer Science and Business Media LLC
Date: 24-11-2015
Publisher: IEEE
Date: 12-2010
Publisher: IEEE
Date: 12-2009
Publisher: Elsevier BV
Date: 10-2012
Location: Australia
No related grants have been discovered for Hawlader Abdullah Al-Mamun.